Job Title: AI Engineer (Generative AI & Google Ecosystem)
Location: SFO, CA OR Chicago, IL (2-3days Hybrid)

Note: Locals to CA OR IL only

Duration: 6months

About the Role

We are seeking an innovative and product-driven AI Engineer to join our team and build the next generation of intelligent applications. In this role, you will leverage the power of Google s Gemini models and the broader Google Cloud ecosystem to solve complex problems and create user-centric AI features. You will also heavily utilize open-source frameworks, specifically LangChain and LangGraph, to design, orchestrate, and deploy robust, agentic AI workflows.

If you are passionate about Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and building scalable AI architectures, we want you on our team.

Key Responsibilities

Application Development: Design, build, and deploy generative AI applications powered by Google Gemini (Pro, Flash, and Ultra) and Vertex AI.
Orchestration & Workflow Design: Utilize LangChain to build complex prompt pipelines and RAG systems. Design and implement stateful, multi-agent workflows and cyclical AI processes using LangGraph.
Google Cloud Integration: Architect solutions utilizing the Google Cloud Platform (Google Cloud Platform) ecosystem, including Vertex AI Search and Conversation, BigQuery, Cloud Run, and Google Cloud Storage.
Data Pipeline & RAG Engineering: Ingest, process, and chunk diverse data formats. Implement robust vector search architectures using Google Vertex AI Vector Search or open-source vector databases (e.g., Chroma, Milvus).
Model Optimization: Employ advanced prompt engineering techniques, few-shot learning, and parameter-efficient fine-tuning (PEFT) to optimize Gemini's performance for specific industry use cases.
Evaluation & MLOps: Establish rigorous evaluation metrics for LLM outputs (accuracy, latency, hallucination rates). Deploy models using modern LLMOps practices to ensure observability, scalability, and security.
Collaboration: Work closely with product managers, data engineers, and front-end developers to seamlessly integrate AI features into our core products.

Required Qualifications

Education: Bachelor s or Master s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field (or equivalent practical experience).
Programming: High proficiency in Python, with a strong grasp of software engineering principles, API development (FastAPI/Flask), and asynchronous programming.
AI/LLM Experience: Hands-on experience working directly with LLM APIs (Google Gemini preferred, OpenAI/Anthropic acceptable).
Frameworks: Deep understanding of and practical experience with LangChain and LangGraph for building complex LLM applications and autonomous agents.
Cloud Infrastructure: Proven experience with Google Cloud Platform (Google Cloud Platform), particularly Vertex AI, IAM, and serverless compute functions.
Data & Databases: Experience working with Vector Databases (e.g., Pinecone, Weaviate, Qdrant) and foundational SQL/NoSQL databases.
Problem Solving: Strong analytical skills and the ability to debug complex, non-deterministic AI pipelines.

Nice to Have

Google Cloud Professional Machine Learning Engineer or Professional Cloud Architect certification.
Familiarity with other open-source AI frameworks like LlamaIndex or Hugging Face.
Experience with front-end technologies (React, TypeScript) to rapidly prototype AI-driven user interfaces.
Experience building and deploying multimodal AI applications (handling text, image, and audio inputs).

Need 15+ years of AI Engineer (Generative AI & Google Ecosystem)

Key skills

About this role